The Experts below are selected from a list of 10302 Experts worldwide ranked by ideXlab platform

Toshiyuki Ohtsuka - One of the best experts on this subject based on the ideXlab platform.

  • a continuation gmres method for fast computation of nonlinear receding horizon control
    Automatica, 2004
    Co-Authors: Toshiyuki Ohtsuka
    Abstract:

    In this paper, a fast numerical algorithm for nonlinear receding horizon control is proposed. The control input is updated by a differential equation to trace the solution of an associated state-dependent two-point boundary-value problem. A linear equation involved in the differential equation is solved by the generalized minimum residual method, one of the Krylov subspace methods, with Jacobians approximated by forward differences. The error in the entire algorithm is analyzed and is shown to be bounded under some conditions. The proposed algorithm is applied to a two-Link Arm whose dynamics is highly nonlinear. Simulation results show that the proposed algorithm is faster than the conventional algorithms.

  • continuation gmres method for fast algorithm of nonlinear receding horizon control
    Conference on Decision and Control, 2000
    Co-Authors: Toshiyuki Ohtsuka
    Abstract:

    Proposes a fast algorithm for nonlinear receding horizon control. The control input is updated by a differential equation to trace the solution of an associated two-point boundary-value problem. A linear equation involved in the differential equation is solved by the generalized minimum residual (GMRES) method, one of the Krylov subspace methods, with Jacobians approximated by forward differences. The error in the entire algorithm is analyzed and is shown to be bounded under mild conditions. The proposed algorithm is applied to a two-Link Arm whose dynamics is highly nonlinear.

Antonio Bicchi - One of the best experts on this subject based on the ideXlab platform.

  • vsa ii a novel prototype of variable stiffness actuator for safe and performing robots interacting with humans
    International Conference on Robotics and Automation, 2008
    Co-Authors: R Schiavi, Giorgio Grioli, Soumen Sen, Antonio Bicchi
    Abstract:

    This paper presents design and performance of a novel joint based actuator for a robot run by variable stiffness actuation, meant for systems physically interacting with humans. This new actuator prototype (VSA-II) is developed as an improvement over our previously developed one reported in [9], where an optimal mechanical-control co-design principle established in [7] is followed as well. While the first version was built in a way to demonstrate effectiveness of variable impedance actuation (VIA), it had limitations in torque capacities, life cycle and implementability in a real robot. VSA-II overcomes the problem of implementability with higher capacities and robustness in design for longer life. The paper discusses design and stiffness behaviour of VSA-II in theory and experiments. A comparison of stiffness characteristics between the two actuator is discussed, highlighting the advantages of the new design. A simple, but effective PD scheme is employed to independently control joint-stiffness and joint-position of a 1-Link Arm. Finally, results from performed impact tests of 1- Link Arm are reported, showing the effectiveness of stiffness variation in controlling value of a safety metric.

  • design and control of a variable stiffness actuator for safe and fast physical human robot interaction
    International Conference on Robotics and Automation, 2005
    Co-Authors: G Tonietti, R Schiavi, Antonio Bicchi
    Abstract:

    This paper is concerned with the design and control of actuators for machines and robots physically interacting with humans, implementing criteria established in our previous work [1] on optimal mechanical-control co-design for intrinsically safe, yet performant machines. In our Variable Impedance Actuation (VIA) approach, actuators control in real-time both the reference position and the mechanical impedance of the moving parts in the machine in such a way to optimize performance while intrinsically guaranteeing safety. In this paper we describe an implementation of such concepts, consisting of a novel electromechanical Variable Stiffness Actuation (VSA) motor. The design and the functioning principle of the VSA are reported, along with the analysis of its dynamic behavior. A novel scheme for feedback control of this device is presented, along with experimental results showing performance and safety of a one-Link Arm actuated by the VSA motor.

Yoji Uno - One of the best experts on this subject based on the ideXlab platform.

  • learning of real robot s inverse dynamics by a forward propagation learning rule
    Electrical Engineering in Japan, 2007
    Co-Authors: Hiroki Mori, Yoshihiro Ohama, Naohiro Fukumura, Yoji Uno
    Abstract:

    A forward-propagation learning rule (FPL) has been proposed for a neural network (NN) to learn an inverse model of a controlled object. A feature of FPL is that the trajectory error propagates forward in NN and appropriate values of two learning parameters are required to be set. FPL has only been simulated to several kinds of controlled objects such as a two-Link Arm in a horizontal plane. In this work, we applied FPL to AIBO and showed the validity of FPL on a real controlled object. At first, we tested a learning experiment of an inverse dynamic of a two-Link Arm in a sagittal plane with viscosity and Coulomb friction by computer simulation. In this simulation, a low-pass filter (LPF) was applied to realized trajectories because coulomb friction vibrates them. From the simulation results, we found that the learning process is stable by some adequate sets of the learning parameters although it is more sensitive to the values of the parameters owing to friction and gravity terms. Finally, we tested applying FPL to motor control of AIBO's leg. The inverse dynamics model was acquired by FPL with only about 150 learning iterations. From these results, the validity of the FPL was confirmed by the real robot control experiments. © 2007 Wiley Periodicals, Inc. Electr Eng Jpn, 161(4): 38–48, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20456

  • a forward propagation learning rule for neural inverse models using a method of recursive least squares
    Systems and Computers in Japan, 2005
    Co-Authors: Yoshihiro Ohama, Naohiro Fukumura, Yoji Uno
    Abstract:

    A forward-propagation learning scheme has been proposed to acquire inverse models of controlled objects in multilayered neural networks. This scheme is quite different from back-propagation learning rule. The algorithm of the forward-propagation rule consists of two stages. One is the estimation of the instruction signal at each layer by the Newton-like method, and the other is the updating of the connection weights by linear multiple regression. In this scheme, convergence of learning has been faster than other schemes based on backpropagation rule. However, the problems arise that complex parameters must be set for learning and the learning process is too complex and sometimes stops. This paper proposes to use a method of recursive least squares in the forward-propagation rule. The effectiveness of the proposed method is confirmed by computer simulation for the learning of the inverse dynamics model for a two-Link Arm. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(8): 71–80, 2005; Published online in Wiley InterScience (). DOI 10.1002sscj.20237

  • a forward propagation learning rule for acquiring inverse models in multilayered neural networks
    Electronics and Communications in Japan Part Ii-electronics, 2005
    Co-Authors: Kazuyuki Nagasawa, Naohiro Fukumura, Yoji Uno
    Abstract:

    Many proposals have been presented for the acquisition of inverse models in multilayered neural networks. However, most are concerned with the backpropagation rule or its improvement. In learning in a multilayered neural network based on the backpropagation rule, there must be a supervisor signal for the output layer, and there must be a particular path to propagate the learning signal in the reverse direction. In addition, convergence is slow due to the use of the method of steepest descent in updating the weights. Consequently, this paper proposes a forward-propagation rule in which the neural network model is trained by propagating the motion error exhibited by the control object in the forward direction in the neural network. In the proposed algorithm, the extended Newton's method is used to derive the goal signal (which corresponds to the supervisor signal) in the hidden layer and the output layer. Since linear multiple regression can be used in weight updating for realizing the goal signals, the iteration of weight updating can be reduced compared to the method of steepest descent. A computer simulation was performed for acquisition of a two-Link Arm model, and the effectiveness of the proposed learning scheme was verified. © 2005 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 88(2): 59–68, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.20148

  • learning of real robot s inverse dynamics by a forward propagation learning rule
    Ieej Transactions on Electronics Information and Systems, 2005
    Co-Authors: Hiroki Mori, Yoshihiro Ohama, Naohiro Fukumura, Yoji Uno
    Abstract:

    A forward-propagation learning rule (FPL) has been proposed for Neural Network (NN) to learn an inverse model of a controlled object. A feature of FPL is that the trajectory error propagates forward in NN and appropriate values of two learning parameters are required to be set. FPL has been only simulated to several kinds of controlled objects such as a 2-Link Arm in a horizontal plane. In this work, we applied FPL to AIBO so that we showed validity of FPL on a real controlled object. At first, we tested a learning experiment of an inverse dynamics of a 2-Link Arm in a sagittal plane with viscosity and coulomb friction by computer simulation. In this simulation, low pass filter (LPF) was applied to realized trajectories because coulomb friction vibrates them. From results of simulation, we found that the learning process is stable by some adequate sets of the learning parameters although it is more sensitive to the values of the parameters owing to friction and gravity terms. Finally, we tested applying FPL to motor control of AIBO's leg. The inverse dynamics model was acquired by FPL with only about 150 learning iterations. From these results, the validity of the FPL was confirmed by the real robot control experiments.

  • a forward propagation rule for acquiring neural inverse models using a rls algorithm
    International Conference on Neural Information Processing, 2004
    Co-Authors: Yoshihiro Ohama, Naohiro Fukumura, Yoji Uno
    Abstract:

    It has been suggested that inverse models serve feedforward controllers in the human brain. We have proposed a novel learning scheme to acquire a neural inverse model of a controlled object. This scheme propagates error “forward” in a multi-layered neural network to solve a credit assignment problem based on Newton-like method. In this paper, we apply a RLS algorithm to this scheme for the stability of learning. The suitability of the proposed scheme was confirmed by computer simulation; it could acquire an inverse dynamics model of a 2-Link Arm faster than a conventional scheme based on a back-propagation rule.

Frank L Lewis - One of the best experts on this subject based on the ideXlab platform.

  • two time scale fuzzy logic controller of flexible Link robot Arm
    Fuzzy Sets and Systems, 2003
    Co-Authors: Jianyong Lin, Frank L Lewis
    Abstract:

    Abstract A flexible Link Arm is a distributed parameter system of infinite order, but due to onboard computer limitations, sensor inaccuracy, and system noise, it must be approximated by a lower-order model and controlled by a finite-order controller. The main object of this paper is concentrated on the hierarchical fuzzy logic by the singular perturbation approach for flexible-Link robot Arm control. A composite control design is adopted. Therefore, a two-time scale fuzzy logic controller will be applied for such system. In this paper, the fast-subsystem controller will be damp out the vibration of the flexible structure by two hierarchical fuzzy logic controllers. Moreover, the other slow-subsystem fuzzy controller dominates the trajectory tracking. We guarantee the stability of the internal dynamics by adding a boundary-layer correction based on singular perturbations. In addition, various case studies are given in illustration to verify the control algorithm. It appears that the fuzzy control method is quite useful as regards reliability and robustness.

Hossein Jahanabadi - One of the best experts on this subject based on the ideXlab platform.

  • active force with fuzzy logic control of a two Link Arm driven by pneumatic artificial muscles
    Journal of Bionic Engineering, 2011
    Co-Authors: Hossein Jahanabadi, Musa Mailah, M Md Z Zain, H M Hooi
    Abstract:

    In this paper, the practicality and feasibility of Active Force Control (AFC) integrated with Fuzzy Logic(AFCAFL) applied to a two Link planar Arm actuated by a pair of Pneumatic Artificial Muscle (PAM) is investigated. The study emphasizes on the application and control of PAM actuators which may be considered as the new generation of actuators comprising fluidic muscle that has high-tension force, high power to weight ratio and high strength in spite of its drawbacks in the form of high nonlinearity behaviour, high hysteresis and time varying parameters. Fuzzy Logic (FL) is used as a technique to estimate the best value of the inertia matrix of robot Arm essential for the AFC mechanism that is complemented with a conventional Proportional-Integral-Derivative (PID) control at the outermost loop. A simulation study was first performed followed by an experimental investigation for validation. The experimental study was based on the independent joint tracking control and coordinated motion control of the Arm in Cartesian or task space. In the former, the PAM actuated Arm is commanded to track the prescribed trajectories due to hArmonic excitations at the joints for a given frequency, whereas for the latter, two sets of trajectories with different loadings were considered. A practical rig utilizing a Hardware-In-The-Loop Simulation (HILS) configuration was developed and a number of experiments were carried out. The results of the experiment and the simulation works were in good agreement, which verified the effectiveness and robustness of the proposed AFCAFL scheme actuated by PAM.

  • experimental implementation of active force control and iterative learning technique to a two Link Arm driven by penumatic artificial muscles
    Computational Intelligence, 2011
    Co-Authors: Musa Mailah, H H Mun, Suhail Kazi, Hossein Jahanabadi
    Abstract:

    This paper highlights the practical viability and feasibility of an active force control (AFC) technique incorporating an iterative learning (IL) algorithm known as AFCAIL applied to a two-Link planar Arm actuated by a pair of pneumatic artificial muscles (PAM). The robust performance of a robot control scheme is vital to ensure that the robot accomplishes its tasks desirably in a constraint environment involving disturbances, parametric changes, uncertainties and varied operating conditions. Iterative learning (IL) is used as a technique to compute the best value of the inertia matrix of robot Arm required for the AFC loop that is complemented with a conventional proportional-integral-derivative (PID) control at the outermost loop. A practical rig utilizing a hardware-in-the-loop simulation (HILS) configuration was developed and a number of experiments were carried out to validate the theoretical counterpart. The results of the experimental works verify the effectiveness and robustness of the proposed PAM actuated AFCAIL scheme for the given operating and loading conditions.